SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

"I'm pulling through because of you": injured workers' perspective of workplace factors supporting return to work under the Saskatchewan Workers' Compensation Board scheme.

Background: Research demonstrates sustained return to work (RTW) by individuals on medical leave is influenced by personal and job resources and job demands. Relatively few studies have been conducted in the workers' compensation context that is known to have longer absence durations for RTW. Aims: This study sought to illuminate workers' experience as they returned to work following a work injury that was either psychological in nature or involved more than 50 days of disability, with a focus on the co-worker, supervisor, and employer actions that supported their return. Methods: Workers in Saskatchewan, Canada, with a work-related psychological or musculoskeletal injury, subsequent disability, and who returned to work in the last three years, were invited to complete an online survey comprising of free-text questions. Thematic analysis was used to explore participants' experiences. Results: Responses from 93 individuals were analysed. These revealed that persistent pain, emotional distress, and loss of normal abilities were present during and beyond returning to work. Almost two-thirds indicated that the supervisors' and co-workers' support was critical to a sustained return to work: their needs were recognized and they received autonomy and support to manage work demands. By contrast, one-third indicated that the support they expected and needed from supervisors and employers was lacking. Conclusions: Workers returning to work lacked personal resources but co-workers' and supervisors' support helped improve confidence in their ability to RTW. Supervisors and employers should acknowledge workers' experiences and offer support and autonomy. Likewise, workers can expect challenges when returning to work and may benefit from cultivating supportive relationships with co-workers and supervisors.

MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data.

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts.

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Butyrate reduces adherent-invasive E. coli-evoked disruption of epithelial mitochondrial morphology and barrier function: involvement of free fatty acid receptor 3.

Gut bacteria provide benefits to the host and have been implicated in inflammatory bowel disease (IBD), where adherent-invasive E. coli (AIEC) pathobionts (e.g., strain LF82) are associated with Crohn's disease. E. coli-LF82 causes fragmentation of the epithelial mitochondrial network, leading to increased epithelial permeability. We hypothesized that butyrate would limit the epithelial mitochondrial disruption caused by E. coli-LF82. Human colonic organoids and the T84 epithelial cell line infected with E. coli-LF82 (MOI = 100, 4 h) showed a significant increase in mitochondrial network fission that was reduced by butyrate (10 mM) co-treatment. Butyrate reduced the loss of mitochondrial membrane potential caused by E. coli-LF82 and increased expression of PGC-1α mRNA, the master regulator of mitochondrial biogenesis. Metabolomics revealed that butyrate significantly altered E. coli-LF82 central carbon metabolism leading to diminished glucose uptake and increased succinate secretion. Correlating with preservation of mitochondrial network form/function, butyrate reduced E. coli-LF82 transcytosis across T84-cell monolayers. The use of the G-protein inhibitor, pertussis toxin, implicated GPCR signaling as critical to the effect of butyrate, and the free fatty acid receptor three (FFAR3, GPR41) agonist, AR420626, reproduced butyrate's effect in terms of ameliorating the loss of barrier function and reducing the mitochondrial fragmentation observed in E. coli-LF82 infected T84-cells and organoids. These data indicate that butyrate helps maintain epithelial mitochondrial form/function when challenged by E. coli-LF82 and that this occurs, at least in part, via FFAR3. Thus, loss of butyrate-producing bacteria in IBD in the context of pathobionts would contribute to loss of epithelial mitochondrial and barrier functions that could evoke disease and/or exaggerate a low-grade inflammation.

SCALiR: a web application for automating absolute quantification of mass spectrometry-based metabolomics data.

Metabolomics is an important approach for studying complex biological systems. Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming a mainstream strategy but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process which is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (Standard Curve Application for determining Linear Ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signal data into absolute quantitative data (https://www.lewisresearchgroup.org/software). The algorithm used in SCALiR automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from their LC-MS signal. Using a standard mix containing 77 metabolites, we found excellent correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99) and that SCALiR reproducibly calculated concentrations of mid-range standards across ten analytical batches (average coefficient of variation 0.091). SCALiR offers users several advantages, including that it (1) is open-source and vendor agnostic; (2) requires only 10 seconds of analysis time to compute concentrations of >75 compounds; (3) facilitates automation of quantitative workflows; and (4) performs deterministic evaluation of compound quantification limits. SCALiR provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Genome-wide transcription factor-binding maps reveal cell-specific changes in the regulatory architecture of human HSPCs.

Hematopoietic stem and progenitor cells (HSPCs) rely on a complex interplay among transcription factors (TFs) to regulate differentiation into mature blood cells. A heptad of TFs (FLI1, ERG, GATA2, RUNX1, TAL1, LYL1, LMO2) bind regulatory elements in bulk CD34+ HSPCs. However, whether specific heptad-TF combinations have distinct roles in regulating hematopoietic differentiation remains unknown. We mapped genome-wide chromatin contacts (HiC, H3K27ac, HiChIP), chromatin modifications (H3K4me3, H3K27ac, H3K27me3) and 10 TF binding profiles (heptad, PU.1, CTCF, STAG2) in HSPC subsets (stem/multipotent progenitors plus common myeloid, granulocyte macrophage, and megakaryocyte erythrocyte progenitors) and found TF occupancy and enhancer-promoter interactions varied significantly across cell types and were associated with cell-type-specific gene expression. Distinct regulatory elements were enriched with specific heptad-TF combinations, including stem-cell-specific elements with ERG, and myeloid- and erythroid-specific elements with combinations of FLI1, RUNX1, GATA2, TAL1, LYL1, and LMO2. Furthermore, heptad-occupied regions in HSPCs were subsequently bound by lineage-defining TFs, including PU.1 and GATA1, suggesting that heptad factors may prime regulatory elements for use in mature cell types. We also found that enhancers with cell-type-specific heptad occupancy shared a common grammar with respect to TF binding motifs, suggesting that combinatorial binding of TF complexes was at least partially regulated by features encoded in DNA sequence motifs. Taken together, this study comprehensively characterizes the gene regulatory landscape in rare subpopulations of human HSPCs. The accompanying data sets should serve as a valuable resource for understanding adult hematopoiesis and a framework for analyzing aberrant regulatory networks in leukemic cells.

Metabolic Interactive Nodular Network for Omics (MINNO): Refining and investigating metabolic networks based on empirical metabolomics data.

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms, and metabolic network modeling is commonly used to frame results in the context of a broader homeostatic system. However, network modeling of poorly characterized, non-model organisms remains challenging due to gene homology mismatches. To address this challenge, we developed Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that takes in empirical metabolomics data to refine metabolic networks for both model and unusual organisms. MINNO allows users to create and modify interactive metabolic pathway visualizations for thousands of organisms, in both individual and multi-species contexts. Herein, we demonstrate an important application of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme disease and relapsing fever. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for three Borrelia species. Using these empirically refined networks, we were able to metabolically differentiate these genetically similar species via their nucleotide and nicotinate metabolic pathways that cannot be predicted from genomic networks. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study non-model organisms.

Modelling the cost-effectiveness of subepidermal moisture measurement as part of a process of assessment and intervention to prevent hospital-acquired pressure ulcers.

Skin tissue assessment is traditionally used to identify early signs of pressure damage from changes observed at the skin surface. However, the early onset of tissue damage induced by pressure and shear forces is likely to be on soft tissues beneath the surface of the skin. Subepidermal moisture (SEM) is a biophysical marker for the detection of early and deep pressure-induced tissue damage. Measurement of SEM can detect early pressure ulcers up to 5 days before visible skin changes occur. The aim of this study was to evaluate the cost-effectiveness of SEM measurement compared with visual skin assessment (VSA). A decision-tree model was developed. Outcomes are the incidence of hospital-acquired pressure ulcers, quality-adjusted life-years (QALYs) and costs to the UK National Health Service. Costs are at 2020/21 prices. The effects of parameter uncertainty are tested in univariate and probabilistic sensitivity analysis. In a representative NHS acute hospital, the incremental cost of SEM assessment as an adjunct to VSA is -£8.99 per admission, and SEM assessment is expected to reduce the incidence of hospital-acquired pressure ulcers by 21.1%, reduce NHS costs and lead to a gain of 3.634 QALYs. The probability of cost-effectiveness at a threshold of £30 000 per quality-adjusted life year is 61.84%. Pathways that include SEM assessment make it possible to implement early and anatomy-specific interventions which have the potential to improve the effectiveness of pressure ulcer prevention and reduce healthcare costs.

Calibration of cognitive tests to address the reliability paradox for decision-conflict tasks.

Standard, well-established cognitive tasks that produce reliable effects in group comparisons also lead to unreliable measurement when assessing individual differences. This reliability paradox has been demonstrated in decision-conflict tasks such as the Simon, Flanker, and Stroop tasks, which measure various aspects of cognitive control. We aim to address this paradox by implementing carefully calibrated versions of the standard tests with an additional manipulation to encourage processing of conflicting information, as well as combinations of standard tasks. Over five experiments, we show that a Flanker task and a combined Simon and Stroop task with the additional manipulation produced reliable estimates of individual differences in under 100 trials per task, which improves on the reliability seen in benchmark Flanker, Simon, and Stroop data. We make these tasks freely available and discuss both theoretical and applied implications regarding how the cognitive testing of individual differences is carried out.

Microbiota alters the metabolome in an age- and sex- dependent manner in mice.

Commensal bacteria are major contributors to mammalian metabolism. We used liquid chromatography mass spectrometry to study the metabolomes of germ-free, gnotobiotic, and specific-pathogen-free mice, while also evaluating the influence of age and sex on metabolite profiles. Microbiota modified the metabolome of all body sites and accounted for the highest proportion of variation within the gastrointestinal tract. Microbiota and age explained similar amounts of variation the metabolome of urine, serum, and peritoneal fluid, while age was the primary driver of variation in the liver and spleen. Although sex explained the least amount of variation at all sites, it had a significant impact on all sites except the ileum. Collectively, these data illustrate the interplay between microbiota, age, and sex in the metabolic phenotypes of diverse body sites. This provides a framework for interpreting complex metabolic phenotypes and will help guide future studies into the role that the microbiome plays in disease.

Staphylococcus aureus stimulates neutrophil itaconate production that suppresses the oxidative burst.

Neutrophils are critical in the host defense against Staphylococcus aureus, a major human pathogen. However, even in the setting of a robust neutrophil response, S. aureus can evade immune clearance. Here, we demonstrate that S. aureus impairs neutrophil function by triggering the production of the anti-inflammatory metabolite itaconate. The enzyme that synthesizes itaconate, Irg1, is selectively expressed in neutrophils during S. aureus pneumonia. Itaconate inhibits neutrophil glycolysis and oxidative burst, which impairs survival and bacterial killing. In a murine pneumonia model, neutrophil Irg1 expression protects the lung from excessive inflammation but compromises bacterial clearance. S. aureus is thus able to evade the innate immune response by targeting neutrophil metabolism and inducing the production of the anti-inflammatory metabolite itaconate.

Clearance of plasma PCSK9 via the asialoglycoprotein receptor mediated by heterobifunctional ligands.

Proprotein convertase subtilisin/kexin type 9 (PCSK9) regulates plasma low-density lipoprotein cholesterol (LDL-C) levels by promoting the degradation of hepatic LDL receptors (LDLRs). Current therapeutic approaches use antibodies that disrupt PCSK9 binding to LDLR to reduce circulating LDL-C concentrations or siRNA that reduces PCSK9 synthesis and thereby levels in circulation. Recent reports describe small molecules that, like therapeutic antibodies, interfere with PCSK9 binding to LDLR. We report an alternative approach to decrease circulating PCSK9 levels by accelerating PCSK9 clearance and degradation using heterobifunctional molecules that simultaneously bind to PCSK9 and the asialoglycoprotein receptor (ASGPR). Various formats, including bispecific antibodies, antibody-small molecule conjugates, and heterobifunctional small molecules, demonstrate binding in vitro and accelerated PCSK9 clearance in vivo. These molecules showcase a new approach to PCSK9 inhibition, targeted plasma protein degradation (TPPD), and demonstrate the feasibility of heterobifunctional small molecule ligands to accelerate the clearance and degradation of pathogenic proteins in circulation.

Global oral poliovirus vaccine stockpile management as an essential preparedness and response mechanism for type 2 poliovirus outbreaks following global oral poliovirus vaccine type 2 withdrawal.

Following the global declaration of indigenous wild poliovirus type 2 eradication in 2015, the world switched to oral polio vaccine (OPV) that removed the type 2 component. This 'switch' included the widespread introduction of inactivated poliovirus vaccine and the creation of a stockpile of monovalent type 2 OPV (mOPV2) to respond to potential polio virus Type 2 (PV2) outbreaks and events. With subsequent detection of outbreaks of circulating vaccine-derived poliovirus type 2 (cVDPV2), it was necessary to use this stockpile for outbreak response. Not only were more outbreaks detected than anticipated in the first few years after the switch, but the number of supplemental immunization activities (SIAs) used to stop transmission was often high, and in many cases did not stop wider transmission. Use of mOPV type 2 led in some locations to the emergence of new outbreaks that required further use of the vaccine from the stockpile. In the following years, stockpile management became a critical element of the cVDPV2 outbreak response strategy and continued to evolve to include trivalent OPV and genetically stabilized 'novel OPV type 2' vaccines in the stockpile. An overview of this process and its evolution is presented to highlight several of these management challenges. The unpredictable vaccine demand, fixed production and procurement timelines, resource requirements, and multiple vaccine types contributes to the complexity of assuring appropriate vaccine availability for this critical programmatic need to stop outbreaks.

Rapid LC-MS assay for targeted metabolite quantification by serial injection into isocratic gradients.

Liquid chromatography mass spectrometry (LC-MS) has emerged as a mainstream strategy for metabolomics analyses. One advantage of LC-MS is that it can serve both as a biomarker discovery tool and as a platform for clinical diagnostics. Consequently, it offers an exciting opportunity to potentially transition research studies into real-world clinical tools. One important distinction between research versus diagnostics-based applications of LC-MS is throughput. Clinical LC-MS must enable quantitative analyses of target molecules in hundreds or thousands of samples each day. Currently, the throughput of these clinical applications is limited by the chromatographic gradient lengths, which-when analyzing complex metabolomics samples-are difficult to conduct in under ~ 3 min per sample without introducing serious quantitative analysis problems. To address this shortcoming, we developed sequential quantification using isotope dilution (SQUID), an analytical strategy that combines serial sample injections into a continuous isocratic mobile phase to maximize throughput. SQUID uses internal isotope-labelled standards to correct for changes in LC-MS response factors over time. We show that SQUID can detect microbial polyamines in human urine specimens (lower limit of quantification; LLOQ = 106 nM) with less than 0.019 normalized root mean square error. Moreover, we show that samples can be analyzed in as little as 57 s. We propose SQUID as a new, high-throughput LC-MS tool for quantifying small sets of target biomarkers across large cohorts.

Direct Measurement of Chocolate Components Using Dispersive Raman Spectroscopy at 1000 nm Excitation.

Chocolate is a popular food around the world. Making chocolate-based confectionaries involve multiple processing steps including cocoa bean fermentation, cocoa bean roasting, grinding, and then a controlled crystallization, where the processing conditions yields the desirable polymorph V to give chocolate its characteristic snap and texture. Raman spectroscopy is well known as a technique that can provide a non-contact, non-destructive analysis of chemical composition and molecular structure. Yet, excitation in the visible and near-infrared (532-785 nm) has not been possible for dark or milk chocolate because of the samples' overwhelming fluorescence. New technologies enabling Raman spectroscopy closer to shortwave infrared wavelengths, closer to 1000 nm, are likely to reduce fluorescence of chocolate and other highly fluorescent samples. Based on the successes of 1064 nm excitation to understand chocolate blooming, we hypothesized that 1000 nm excitation would also reduce fluorescence and enable Raman spectroscopy in dark and milk chocolates. We used dispersive Raman spectroscopy at 1000 nm to measure white, milk, and dark chocolate and cocoa nibs. The use of 1000 nm excitation effectively reduced fluorescence, enabling qualitative and quantitative Raman spectroscopy directly on chocolate samples. These feasibility studies indicate that 1000 nm Raman spectroscopy can be used to measure chocolate in a laboratory or process environment.

Identifying research priorities in breast cancer surgery: a UK priority setting partnership with the James Lind Alliance.

PURPOSE: A James Lind Alliance priority setting partnership was developed to identify research priorities in breast cancer surgery from individuals with lived experience, at high genetic risk of breast cancer, and healthcare professionals (HCPs). METHODS: 'Uncertainties' were collected using an online survey. Following an evidence check and development of summary questions, an interim survey asked participants to rank their top 10 research priorities from the question list. Top-ranked questions from patient/carer, high-risk and professional groups were carried forward for discussion to a final online prioritisation workshop. RESULTS: 260 participants (101 patients/carers, 156 HCPs) submitted 940 uncertainties via the initial survey. These were analysed thematically into 128 summary questions in six topic areas. Following evidence checking, 59 questions were included in the interim survey which was completed by 572 respondents. Marked differences were seen in questions prioritised by patients/carers, HCPs and women at high-risk. The top eight priorities in patient/carer and professional groups and top two priorities for high-risk women were carried forward to the online workshop at which 22 participants discussed and agreed the final top 10. Key themes included de-escalation of breast and axillary surgery, factors impacting the development/detection of locoregional recurrence and optimal provision of support for informed treatment decision-making. CONCLUSION: The top 10 research priorities in breast cancer surgery have been agreed. However, the observed differences in research priorities identified by patients and professional groups were not anticipated. Top priorities from both groups should inform future UK breast cancer surgical research, to ensure that it addresses questions that are important to breast cancer community as a whole.

No effect of short term exposure to gambling like reward systems on post game risk taking.

Is engaging with gambling-like video game rewards a risk factor for future gambling? Despite speculation, there are no direct experimental tests of this "gateway hypothesis". We test a mechanism that might support this pathway: the effects of engaging with gambling-like reward mechanisms on risk-taking. We tested the hypothesis that players exposed to gambling-like rewards (i.e., randomised rewards delivered via a loot box) would show increased risk-taking compared to players in fixed and no reward control conditions. 153 participants (Mage = 25) completed twenty minutes of gameplay-including exposure to one of the three reward conditions-before completing a gamified, online version of the Balloon Analogue Risk Task (BART). Self-reports of gambling and loot box engagement were collected via the Problem Gambling Severity Index, and Risky Loot-Box Index. Bayesian t-tests comparing BART scores across reward conditions provided moderate to strong evidence for a null effect of condition on risk-taking (BF = 4.05-10.64). Null effects were not moderated by players' problem gambling symptomatology. A Spearman correlation between past loot box engagement and self-reported gambling severity (rs = 0.35) aligned with existing literature. Our data speak against a "gateway" hypothesis, but add support to the notion that problem gambling symptoms might make players vulnerable to overspending on loot boxes.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation.

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes - the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system-the Microbial Containment Device (MCD)-that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Biomarker enrichment medium: A defined medium for metabolomic analysis of microbial pathogens.

Microbes have diverse metabolic capabilities and differences in these phenotypes are critical for differentiating strains, species, and broader taxa of microorganisms. Recent advances in liquid chromatography-mass spectrometry (LC-MS) allow researchers to track the complex combinations of molecules that are taken up by each cell type and to quantify the rates that individual metabolites enter or exit the cells. This metabolomics-based approach allows complex metabolic phenotypes to be captured in a single assay, enables computational models of microbial metabolism to be constructed, and can serve as a diagnostic approach for clinical microbiology. Unfortunately, metabolic phenotypes are directly affected by the molecular composition of the culture medium and many traditional media are subject to molecular-level heterogeneity. Herein, we show that commercially sourced Mueller Hinton (MH) medium, a Clinical and Laboratory Standards Institute (CLSI) approved medium for clinical microbiology, has significant lot-to-lot and supplier-to-supplier variability in the concentrations of individual nutrients. We show that this variability does not affect microbial growth rates but does affect the metabolic phenotypes observed in vitro-including metabolic phenotypes that distinguish six common pathogens. To address this, we used a combination of isotope-labeling, substrate exclusion, and nutritional supplementation experiments using Roswell Park Memorial Institute (RPMI) medium to identify the specific nutrients used by the microbes to produce diagnostic biomarkers, and to formulate a Biomarker Enrichment Medium (BEM) as an alternative to complex undefined media for metabolomics research, clinical diagnostics, antibiotic susceptibility testing, and other applications where the analysis of stable microbial metabolic phenotypes is important.